Abstract
In the past few years, Evolutionary Algorithms (EAs) based UAV path planners have drawn increasing research interests. However, they are not scalable to large-scale problems, i.e., lots of waypoints. Recently, we have proposed a novel EA-based framework, named Separately Evolving Waypoints (SEW), that can deal with large-scale problems. However, the difficulty of UAV path planning depends not only on the number of waypoints, but on the number of constraints it has to satisfy, especially the number of obstacles. In particular, the number of waypoints required is also partly determined by the number of constraints. Hence, it is critical to further improve SEW with respect to large number of obstacles. Originally, a state-of-the-art global optimization
approach is employed. In this work, we discuss how the increasing number of obstacles will deteriorate the performance of the global optimizer, then we propose multi-modal optimization approaches that facilitates the performance of SEW against large number of obstacles.
approach is employed. In this work, we discuss how the increasing number of obstacles will deteriorate the performance of the global optimizer, then we propose multi-modal optimization approaches that facilitates the performance of SEW against large number of obstacles.
Original language | English |
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Title of host publication | Proceedings of the 2016 IEEE Congress on Evolutionary Computation |
Publisher | IEEE Computer Society Press |
Pages | 1735-1742 |
ISBN (Print) | 978-1-5090-0622-9 |
DOIs | |
Publication status | Published - 24 Jul 2016 |
Event | 2016 IEEE Congress on Evolutionary Computation (CEC) - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 http://www.wcci2016.org/ |
Conference
Conference | 2016 IEEE Congress on Evolutionary Computation (CEC) |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Internet address |